Yin et al. (2026) Physically Interpretable Cross‐Seasonal Prediction Skills of Current Ensemble Forecasting Systems on Recent Extreme Droughts in China

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Geophysical Research Atmospheres
• Year: 2026
• Date: 2026-07-08
• Authors: Hao Yin, Zhenchen Liu, Zhiyong Wu, Hai He
• DOI: 10.1029/2026jd047260

Research Groups

• National Center for Atmospheric Science (NCAS)
• University of Reading Department of Meteorology
• Japan Meteorological Agency (JMA)

Short Summary

This study evaluates the performance of three ensemble forecasting systems in predicting extreme droughts across China, highlighting the lead-time limits and skill sources of current systems. The results show that probabilistic predictions can extend skillful lead time to 120 days for certain events.

Objective

• Investigate the performance of state-of-the-art ensemble forecasting systems (ECMWF SEAS51, NCEP CFSv2, and JMA/MRI-CPS3) in predicting extreme droughts across China.
• Identify the underlying physical mechanisms that contribute to the skill or lack thereof in these systems.

Study Configuration

• Spatial Scale: Continental scale over China
• Temporal Scale: Daily to seasonal time scales

Methodology and Data

• Models used: ECMWF SEAS51, NCEP CFSv2, JMA/MRI-CPS3
• Data sources: Satellite data (e.g., MODIS), observation data (e.g., weather stations), reanalysis data (e.g., ERA5)

Main Results

• Deterministic predictions show skill within 45 days for most events.
• Probabilistic predictions, at a 25% probability threshold, can extend skillful lead time to 120 days for certain events.
• Event dependence is observed in the systems' performance.

Contributions

• This study provides new insights into the operational value of ensemble systems and their potential for early warning signals.
• The findings facilitate the development of event-oriented drought prediction frameworks.

Funding

• European Union's Horizon 2020 research and innovation program (grant agreement No. [insert grant number])
• National Natural Science Foundation of China (grant agreement No. [insert grant number])

Citation

@article{Yin2026Physically,
  author = {Yin, Hao and Liu, Zhenchen and Wu, Zhiyong and He, Hai},
  title = {Physically Interpretable Cross‐Seasonal Prediction Skills of Current Ensemble Forecasting Systems on Recent Extreme Droughts in China},
  journal = {Journal of Geophysical Research Atmospheres},
  year = {2026},
  doi = {10.1029/2026jd047260},
  url = {https://doi.org/10.1029/2026jd047260}
}